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Application of machine learning methods for predicting the risk of stroke occurrence

Назва	Application of machine learning methods for predicting the risk of stroke occurrence
Назва англійською	Application of machine learning methods for predicting the risk of stroke occurrence
Автори	Liubomyr-Oleksii Chereshchuk, Nataliia Melnykova
Принадлежність	Lviv Polytechnic National University, Lviv, Ukraine
Бібліографічний опис	Application of machine learning methods for predicting the risk of stroke occurrence / Liubomyr-Oleksii Chereshchuk, Nataliia Melnykova // Scientific Journal of TNTU. — Tern.: TNTU, 2024. — Vol 113. — No 1. — P. 27–35.
Bibliographic description:	Chereshchuk L.-O., Melnykova N. (2024) Application of machine learning methods for predicting the risk of stroke occurrence. Scientific Journal of TNTU (Tern.), vol 113, no 1, pp. 27–35.
DOI:	https://doi.org/10.33108/visnyk_tntu2024.01.027
УДК	004.89: 002.53
Ключові слова	machine learning, stroke, decision tree, random forest, k-neighbors, ada boost, stacking, SMOTE, grid search.
	In the paper, research was carried out in the medical field, which is very important for people and is gaining more and more importance every year. The study was aimed at predicting the occurrence of a stroke, this disease is a serious threat to people's health and lives. To build machine learning models that could solve the problem of predicting the occurrence of a stroke, a very unbalanced dataset was used, which made the work difficult. The best results were shown by the Random Forest model, which reached precision, recall, and f1-score equal to 90%. The obtained results can be useful for doctors and medical workers engaged in the diagnosis and treatment of stroke.
ISSN:	2522-4433
Перелік літератури	1. Mostafa S. A., Elzanfaly D. S., Yakoub A. E. A Machine Learning Ensemble Classifier for Prediction of Brain Strokes. International Journal of Advanced Computer Science and Applications (IJACSA). 2022. Issue 13. No. 12. [In English]. 2. Biswas N., Uddin K. M. M., Rikta S. T., Dey S. K. A comparative analysis of machine learning classifiers for stroke prediction: A predictive analytics approach. Healthcare Analytics. 2022. Issue 2. P. 100116. [In English]. 3. Dritsas E., Trigka M. Stroke Risk Prediction with Machine Learning Techniques. Sensors. 2022. Issue 22. No. 13. P. 4670. [In English]. 4. Khan M. K. Computer Science and Engineering. 2022. [In English]. 5. Sailasya G., Kumari G. L. A. Analyzing the Performance of Stroke Prediction using ML Classification Algorithms. International Journal of Advanced Computer Science and Applications (IJACSA). 2021. Issue 12. No. 6. [In English]. 6. DataHack : Biggest Data hackathon platform for Data Scientists. Web Resource. [In English]. URL: https://datahack.analyticsvidhya.com.
References:	1. Mostafa S. A., Elzanfaly D. S., Yakoub A. E. A Machine Learning Ensemble Classifier for Prediction of Brain Strokes. International Journal of Advanced Computer Science and Applications (IJACSA). 2022. Issue 13. No. 12. [In English]. 2. Biswas N., Uddin K. M. M., Rikta S. T., Dey S. K. A comparative analysis of machine learning classifiers for stroke prediction: A predictive analytics approach. Healthcare Analytics. 2022. Issue 2. P. 100116. [In English]. 3. Dritsas E., Trigka M. Stroke Risk Prediction with Machine Learning Techniques. Sensors. 2022. Issue 22. No. 13. P. 4670. [In English]. 4. Khan M. K. Computer Science and Engineering. 2022. [In English]. 5. Sailasya G., Kumari G. L. A. Analyzing the Performance of Stroke Prediction using ML Classification Algorithms. International Journal of Advanced Computer Science and Applications (IJACSA). 2021. Issue 12. No. 6. [In English]. 6. DataHack : Biggest Data hackathon platform for Data Scientists. Web Resource. [In English]. URL: https://datahack.analyticsvidhya.com.
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